If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Machine learning is the hot buzzword of the last couple years. While some applications have been a little strange, it's likely that machine learning can make shopping a little easier by inferring a shopper's interests based on their history with the store. Valve unveiled Steam Labs to share its machine learning results with the world. Two of the Steam Labs experiments revolve around video content. The Micro Trailers experiment employs deep learning to automatically trim official game trailers into six-second video clips.
Video game developers have longed used artificial intelligence to help create believable worlds. So it's not too surprising that researchers can now use some of those same game-making tools to train AI. During a talk at VentureBeat's Transform 2019 conference last week, Unity Technologies VP of AI and machine learning Danny Lange argued that game engines are perfect for creating what he called "real" computer intelligence -- self-learning systems capable of producing complex behaviors after a short amount of time. With game engines (like the company's own Unity engine), you can simulate the rules of the real world and test intelligent agents against it. "If you think about [it], the game engine has three dimensions, time, physics … it has everything you need to play around with the core elements that led to [human] intelligence," said Lange.
As machine learning (ML) systems continue to improve, its integration to systems making up the society becomes more seamless. Right now, ML is involved in making critical decisions such as court decisions and job hirings. Without a doubt, using ML in these processes will lead to more efficiency. With a good design, ML systems can also eliminate the biases humans have when it comes to their decisions. On the other extreme, this integration could end up really ugly.
Abstract: In the twilight of Moore's law, GPUs and other specialized hardware accelerators have dramatically sped up neural network training. However, earlier stages of the training pipeline, such as disk I/O and data preprocessing, do not run on accelerators. As accelerators continue to improve, these earlier stages will increasingly become the bottleneck. In this paper, we introduce "data echoing," which reduces the total computation used by earlier pipeline stages and speeds up training whenever computation upstream from accelerators dominates the training time. Data echoing reuses (or "echoes") intermediate outputs from earlier pipeline stages in order to reclaim idle capacity.
Abstract: We can define a neural network that can learn to recognize objects in less than 100 lines of code. However, after training, it is characterized by millions of weights that contain the knowledge about many object types across visual scenes. Such networks are thus dramatically easier to understand in terms of the code that makes them than the resulting properties, such as tuning or connections. In analogy, we conjecture that rules for development and learning in brains may be far easier to understand than their resulting properties. The analogy suggests that neuroscience would benefit from a focus on learning and development.
The total amount of power consumed for machine learning tasks is staggering. Until a few years ago we did not have computers powerful enough to run many of the algorithms, but the repurposing of the GPU gave the industry the horsepower that it needed. The problem is that the GPU is not well suited to the task, and most of the power consumed is waste. While machine learning has provided many benefits, much bigger gains will come from pushing machine learning to the edge. To get there, power must be addressed. "You read about how datacenters may consume 5% of the energy today," says Ron Lowman, product marketing manager for Artificial Intelligence at Synopsys.
BCI: Science&Practice is the only annual international conference in Russia with the focus on direct brain-machine interaction. Since October 2015 it is annually organized by Samara State Medical University and IT Universe Ltd in Samara, where a wide range of healthcare technologies, including brain-computer interfaces, virtual reality and other modern IT applications are developed . The conference is supported by Department of Information Technologies of Samara Region and Neuronet Industrial Union. The organizing and program committees members are leading scientists, representatives of state and non-commercial organizations, innovative companies. Attendance is free of charge.
SoFi, directed with the help of a Super Nintendo controller paired with acoustic signals, has been engineered to help researchers explore marine life more freely in depth, and help us get closer to the expansive ecosystem that blooms beyond what our naked eyes can perceive. SoFi is essentially a soft robotic fish structure that consists of a controller, Raspberry Pi, and HiFi Berry, sealed inside a water proof silicone membrane that has been cast moulded. The membrane is also filled with a mineral oil that is non conductive, and allows for equalization underwater. The Raspberry Pi receives input from controller, after which ultrasound signals are amplified for SoFi through the HiFi Berry. These amplified ultrasound signals, which are interpreted by a modem embedded within SoFi's head, controls everything from directing tail movement, pitch and depth, to the on-board camera.
Organizations that are working with artificial intelligence (AI) or machine learning (ML) have, on average, four AI/ML projects in place, according to a recent survey by Gartner, Inc. Of all respondents, 59% said that they have AI deployed today. The Gartner "AI and ML Development Strategies" study was conducted via an online survey in December 2018 with 106 Gartner Research Circle Members – a Gartner-managed panel composed of IT and IT/business professionals. Participants were required to be knowledgeable about the business and technology aspects of ML or AI either currently deployed or in planning at their organizations. "We see a substantial acceleration in AI adoption this year," said Jim Hare, research vice president at Gartner.